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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.23639 |
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| _version_ | 1866915619067133952 |
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| author | Amar, Jonathan Liu, Edward Breschi, Alessandra Zhang, Liangliang Kheradpour, Pouya Li, Sylvia Lehmann, Lisa Soleymani Giulianelli, Alessandro Edwards, Matt Jia, Yugang Nola, David Mani, Raghav Vats, Pankaj Tetreault, Jesse Chen, T. J. McLean, Cory Y. |
| author_facet | Amar, Jonathan Liu, Edward Breschi, Alessandra Zhang, Liangliang Kheradpour, Pouya Li, Sylvia Lehmann, Lisa Soleymani Giulianelli, Alessandro Edwards, Matt Jia, Yugang Nola, David Mani, Raghav Vats, Pankaj Tetreault, Jesse Chen, T. J. McLean, Cory Y. |
| contents | This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Genomics into Multimodal EHR Foundation Models Amar, Jonathan Liu, Edward Breschi, Alessandra Zhang, Liangliang Kheradpour, Pouya Li, Sylvia Lehmann, Lisa Soleymani Giulianelli, Alessandro Edwards, Matt Jia, Yugang Nola, David Mani, Raghav Vats, Pankaj Tetreault, Jesse Chen, T. J. McLean, Cory Y. Machine Learning Artificial Intelligence Quantitative Methods This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare. |
| title | Integrating Genomics into Multimodal EHR Foundation Models |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2510.23639 |